Methods Inf Med 1995; 34(04): 345-351
DOI: 10.1055/s-0038-1634611
Original Article
Schattauer GmbH

Automated Coding of Patient Discharge Summaries Using Conceptual Graphs

D. Delamarre
1   Laboratoire Informatique Médicale, Faculté de Médecine, Université de Rennes I, France
,
A. Burgun
1   Laboratoire Informatique Médicale, Faculté de Médecine, Université de Rennes I, France
,
L. P. Seka
1   Laboratoire Informatique Médicale, Faculté de Médecine, Université de Rennes I, France
,
P. Le Beux
1   Laboratoire Informatique Médicale, Faculté de Médecine, Université de Rennes I, France
› Author Affiliations
Further Information

Publication History

Publication Date:
16 February 2018 (online)

Abstract:

In medicine, as in other domains, indexing and classification is a natural human task which is used for information retrieval and representation. In the medical field, encoding of patient discharge summaries is still a manual time-consuming task. This paper describes an automated coding system of patient discharge summaries from the field of coronary diseases into the ICD-9-CM classification. The system is developed in the context of the European AIM MENELAS project, a natural-language understanding system which uses the conceptual-graph formalism. Indexing is performed by using a two-step processing scheme; a first recognition stage is implemented by a matching procedure and a secondary selection stage is made according to the coding priorities. We show the general features of the necessary translation of the classification terms in the conceptual-graph model, and for the coding rules compliance. An advantage of the system is to provide an objective evaluation and assessment procedure for natural-language understanding.

 
  • References

  • 1 Zweigenbaum P. Consortium Menelas. MENELAS: An access system for medical records using natural language. Comput Meth Progr Biomed 1994; 45: 117-20.
  • 2 Rossi Mori A, Gangemi A, Galanti M. The coding cage. In: Reichertz A, Sadan BA, Bengtsson S, Bryant J, Piccolo U. eds. Proceedings of Informatics in Europe 93. London: Freund Publishing House Ltd; 1993: 466-72.
  • 3 Department of Health and Human Services. International Classification of Diseases – Clinical Modifications. 9th Revision Washington: Department of Health and Human Services; 1980
  • 4 Université Catholique de Louvain, Département des Sciences Hospitalieres et Médico Sociales. Adaptation Hospitaliére de la Classification Internationale des Maladies et des Opérations. Bruxelles: Centre d’informatique médicale de l’Universite Catholique de Louvain; 1990
  • 5 Rossi CP, Alberti V, Mancino G. et al. Comparison between manual and automatic coding of medical record statistical cards at a university hospital. Med Inf (Lond) 1993; 18: 53-9.
  • 6 Baud RH, Rassinoux AM, Scherrer JR. Natural language processing and medical texts. Meth Inform Med 1992; 31: 117-25.
  • 7 Commission of the European Communities, DG XIII/F. AIM Workplan. Call for Proposals for the program R&D in Telematic Systems in Areas of General Interest. 1991
  • 8 Chute CG, Yang Y. An evaluation of concept-based latent semantic indexing for clinical information retrieval. In: Frisse ME. ed. Proceedings of the 16th Annual Symposium on Computer Applications in Medical Care. New York: McGraw Hill; 1992: 639-43.
  • 9 Hersh R, Hickam DH, Leone TJ. Words, concept, or both: Optimal Indexing Units for automated information retrieval. In: Frisse ME. ed. Proceedings of the 16th Annual Symposium on Computer Applications in Medical Care. New York: McGraw Hill; 1992: 644-8.
  • 10 Rassinoux AM, Baud RH, Scherrer JR. Conceptual graphs model extension for knowledge representation of medical texts. In: Lun KC, Degoulet P, Piemme TE, Reinhoff O. eds. MEDINFO 92: Proceeding of the 7th World Congress on Medical Informatics. Amsterdam: North-Holland Publ Comp; 1992: 1368-74.
  • 11 Coté RA. Systematized Nomenclature of Medicine. 2nd ed.. Skokie, IL: College of American Pathologists; 1982
  • 12 Wingert F. An indexing system for SNOMED. Meth Inform Med 1986; 25: 22-30.
  • 13 Satomura Y, Do Amaral MB. Automated diagnostic indexing by natural language processing. Med Inf (Lond) 1992; 17: 149-63.
  • 14 Cimino JJ, Barnett GO. Automated translation between medical terminologies using semantic definitions. MD computing 1990; 7: 104-9.
  • 15 Wingert F. Automated Mapping of ICD into SNOMED. In: Orthner HF, Blum BL. eds. Implementing Health Care Information Systems. New York: Springer-Verlag; 1989: 198-212.
  • 16 Campbell K, Musen M. Representation of clinical data using SNOMED III and conceptual graphs. In: Frisse ME. ed. Proceedings of the 16th Annual Symposium on Computer Applications in Medical Care. New York: McGraw Hill; 1992: 354-8.
  • 17 Lindberg DAB, Humphreys BL, McCray AT. The Unified Medical Language System (UMLS). Meth Inform Med 1993; 32: 281-91.
  • 18 Sowa JF. Conceptual Structures: Information processing in Mind and Machine. New York: Addison-Wesley Publ Comp; 1984
  • 19 Volot F, Zweigenbaum P, Bachimont B. et al. Structuration and acquisition of medical knowledge: Using UMLS in the Conceptual Graph formalism. In: Safran C. ed. Proceedings of the 17th Annual Symposium on Computer Applications in Medical Care. Washington: McGraw Hill; 1993: 710-4.
  • 20 Zweigenbaum P, Bachimont B, Bouaud J. et al. Linguistic and Medical Knowledge Bases. Deliverable report AIM-Menelas 9. Paris: DIAM-SIM/INSERM U.194; 1993
  • 21 Nangle B, Keane MT. Effective retrieval in hospital information systems: the use of context in answering queries to patient discharge summaries. Artif Intell Med 1994; 6: 207-28.